Random sampler M-estimator algorithm for robust function approximation via feed-forward neural networks

This paper addresses the problem of fitting a functional model to data corrupted with outliers using a multilayered feed-forward neural network. The importance of this problem stems from the vast, diverse, practical applications of neural networks as data-driven function approximator or model estimator. Yet, the challenges raised by the presence of outliers in the data have not received the same careful attention from the neural network research community. The paper proposes an enhanced algorithm to train neural networks for robust function approximation in a random sample consensus (RANSAC) framework. The new algorithm follows the same strategy of the original RANSAC algorithm, but employs an M-estimator cost function to decide the best estimated model. The proposed algorithm is evaluated on synthetic data, contaminated with varying degrees of outliers, and compared to existing neural network training algorithms.